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Record W2905883311 · doi:10.3171/2018.7.gks181206

Proceedings of the 2018 next-generation Gamma Knife research meeting

2018· article· en· W2905883311 on OpenAlexafffund
Veronica Chiang, Samuel T. Chao, Constantin Tuleasca, Matthew Foote, Cheng-chia Lee, David Mathieu, Hany Soliman, Arjun Sahgal

Bibliographic record

VenueJournal of neurosurgery · 2018
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversité de Sherbrooke
FundersUniversity of Texas MD Anderson Cancer CenterSamsungStockholms UniversitetBezmialem Vakıf ÜniversitesiSeoul National University HospitalTaipei Veterans General HospitalUniversity of TorontoUniversity of TokyoSeoul National UniversityUniversity of Alberta
KeywordsRadiosurgeryMedicineGamma knifeMedical physicsClinical trialNuclear medicineRadiation therapyRadiologyPathology

Abstract

fetched live from OpenAlex

In order to determine what areas of research are a clinical priority, a small group of young Gamma Knife investigators was invited to attend a workshop discussion at the 19th International Leksell Gamma Knife Society Meeting. Two areas of interest and the need for future radiosurgical research involving multiple institutions were identified by the young investigators working group: 1) the development of additional imaging sequences to guide the understanding, treatment, and outcome tracking of diseases such as tremor, radiation necrosis, and AVM; and 2) trials to clarify the role of hypofractionation versus single-fraction radiosurgery in the treatment of large lesions such as brain metastases, postoperative cavities, and meningiomas.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0440.018

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.190
GPT teacher head0.345
Teacher spread0.155 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2018
Admission routes2
Has abstractyes

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